Dise?o e implementaci?n de una planta automatizada de tratamiento de aguas ?cidas de interior Mina Orcopampa - Compa?ia de Minas del Per?

El proyecto consiste en la construcci?n de una Planta Automatizada de Tratamiento de Aguas ?cidas en la UM Orcopampa, ubicado en el departamento de Arequipa. El mismo es considerado como sostenible debido a que tiene como finalidad, minimizar el impacto de contaminaci?n en las poblaciones aleda?as, ganader?a y agricultura cercanos al proyecto. La planta tratar? las aguas ?cidas del interior de tres bocaminas (Prometida, Nazareno y Rampa Mario), los cuales son vertidos al Rio Chilcaymarca; Para esto se busca elevar el nivel de PH del agua para cumplir con la normativa ambiental vigente. La planta a su vez estar? dividida en planta de 4 reactivos y planta de 1 reactivo, en los cuales se adicionan qu?micos(reactivos) para el tratamiento del agua (cal, floculante, redimex, coagulante y ?cido sulf?rico). Esta adici?n es automatizada para minimizar el consumo de los qu?micos. Para la ejecuci?n del proyecto se determin? utilizar personal propio de CMPSAA y personal tercero. El proyecto tiene una duraci?n de dieciocho meses y un presupuesto de 17 Millones de D?lares

Determinants of penetrance and variable expressivity in monogenic metabolic conditions across 77,184 exomes

Penetrance of variants in monogenic disease and clinical utility of common polygenic variation has not been well explored on a large-scale. Here, the authors use exome sequencing data from 77,184 individuals to generate penetrance estimates and assess the utility of polygenic variation in risk prediction of monogenic variants

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

TFIIE orchestrates the recruitment of the TFIIH kinase module at promoter before release during transcription

The general transcription factors TFIIE and TFIIH assemble at the transcription start site with RNA Polymerase II. Here the authors provide evidence that the TFIIEα and TFIIEβ subunits anchor the TFIIH kinase module within the preinitiation complex before their release during transcription

Polimorfismos del receptor beta-2-adrenérgico y cambios en los lipidos inducidos por metoprolol

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Polimorfismos del receptor beta-2-adrenérgico y cambios en los lipidos inducidos por metoprolol

No disponible en html

Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease.

Funder: BiogenBiobanks facilitate genome-wide association studies (GWASs), which have mapped genomic loci across a range of human diseases and traits. However, most biobanks are primarily composed of individuals of European ancestry. We introduce the Global Biobank Meta-analysis Initiative (GBMI)-a collaborative network of 23 biobanks from 4 continents representing more than 2.2 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWASs generated using harmonized genotypes and phenotypes from member biobanks for 14 exemplar diseases and endpoints. This strategy validates that GWASs conducted in diverse biobanks can be integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics. This collaborative effort improves GWAS power for diseases, benefits understudied diseases, and improves risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of human diseases and traits